Data Preparation

Published

November 21, 2025

Abstract

Here we do some basic data processing and prepare the spatial layers.

Load packages

Code

library(tidyverse)
library(sf)
library(terra)
library(amt)

Load data

Code

dingo_data <- read_csv("data/tanami_collars.csv") %>% filter(x > 0)

Rows: 61359 Columns: 53
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (13): dogname, sex, mine_away, gmt_date, gmt_time, cst_date, cst_month_...
dbl  (38): x, y, dog_id, attempt_id, fix_id, cstmonth, cstmonth_cnt, csthour...
time  (2): cst_time, timemaxwindgist

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Code

dingo_data_sf <- st_as_sf(dingo_data, coords = c("x", "y"), crs = 4326)

Load shapefiles

Code

artificial_food <- st_read("spatial_layers/artificial_food/Art_Food_W84.shp")

Reading layer `Art_Food_W84' from data source 
  `/Users/scottforrest/Library/CloudStorage/OneDrive-QueenslandUniversityofTechnology/PhD - Scott Forrest/GIT/ESA2025_movement_ecology_hackathon/spatial_layers/artificial_food/Art_Food_W84.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 3 features and 2 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 129.6909 ymin: -20.53831 xmax: 130.3135 ymax: -19.98017
Geodetic CRS:  WGS 84

Code

artificial_water <- st_read("spatial_layers/artificial_water/ArtWater_W84v2.shp")

Reading layer `ArtWater_W84v2' from data source 
  `/Users/scottforrest/Library/CloudStorage/OneDrive-QueenslandUniversityofTechnology/PhD - Scott Forrest/GIT/ESA2025_movement_ecology_hackathon/spatial_layers/artificial_water/ArtWater_W84v2.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 91 features and 16 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 129.0532 ymin: -20.74527 xmax: 130.9083 ymax: -19.10321
Geodetic CRS:  WGS 84

Code

roads <- st_read("spatial_layers/roads/RM_rds_trks_clip.shp") %>% 
  st_transform(crs = st_crs(artificial_food))

Reading layer `RM_rds_trks_clip' from data source 
  `/Users/scottforrest/Library/CloudStorage/OneDrive-QueenslandUniversityofTechnology/PhD - Scott Forrest/GIT/ESA2025_movement_ecology_hackathon/spatial_layers/roads/RM_rds_trks_clip.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 1004 features and 1 field
Geometry type: LINESTRING
Dimension:     XY
Bounding box:  xmin: 515871.5 ymin: 7696050 xmax: 713746.3 ymax: 7829996
Projected CRS: GDA94 / MGA zone 52

Plot the shapefiles with dingo GPS data

Dingo locations are shown in a different colour for each ID.

Code

ggplot() +
  geom_sf(data = roads, color = "black") +
  geom_sf(data = artificial_water, color = "blue") +
  geom_sf(data = artificial_food, color = "red") +
  geom_sf(data = dingo_data_sf, aes(color = as.factor(dog_id)), size = 0.25, alpha = 0.25) +
  theme_bw()

Create an AOI for downloading DEM

We can go to this website https://elevation.fsdf.org.au/ to download a DEM for our area of interest (AOI). To do this, we first need to create a rectangle JSON that defines the AOI based on the GPS locations of the dingoes. We will add a buffer around the min and max coordinates to ensure we capture the full area.

In this chunk we create a function that can take a GPS dataset, pull out the location columns to find a max and min in both lat and long (or easting and northing), add a buffer, and create a rectangle polygon shapefile and geojson file.

Code

# Function to create rectangle from coordinate bounds
create_rectangle_shapefile <- function(df, x_col = "x", y_col = "y", 
                                       buffer = 0.05, crs = 4326, 
                                       output_path = "rectangle") {
  
  # Calculate bounds with buffer
  min_x <- min(df[[x_col]], na.rm = TRUE) - buffer
  max_x <- max(df[[x_col]], na.rm = TRUE) + buffer
  min_y <- min(df[[y_col]], na.rm = TRUE) - buffer
  max_y <- max(df[[y_col]], na.rm = TRUE) + buffer
  
  # Create rectangle coordinates (clockwise from bottom-left)
  rectangle_coords <- matrix(c(
    min_x, min_y,  # bottom-left
    max_x, min_y,  # bottom-right
    max_x, max_y,  # top-right
    min_x, max_y,  # top-left
    min_x, min_y   # close the polygon
  ), ncol = 2, byrow = TRUE)
  
  # Create polygon geometry
  rectangle_poly <- st_polygon(list(rectangle_coords))
  
  # Create sf object
  rectangle_sf <- st_sf(
    id = 1,
    area = st_area(rectangle_poly),
    geometry = st_sfc(rectangle_poly, crs = crs)
  )
  
  rectangle_projected <- st_transform(rectangle_sf, crs = 4326) 
  
  # Export as shapefile
  st_write(rectangle_projected, paste0(output_path, ".shp"), 
           delete_dsn = TRUE, quiet = TRUE)
  
  # Also export as GeoJSON (alternative format)
  st_write(rectangle_projected, paste0(output_path, ".geojson"), 
           delete_dsn = TRUE, quiet = TRUE)
  
  # Print summary
  cat("Rectangle created with bounds:\n")
  cat("X range:", min_x, "to", max_x, "\n")
  cat("Y range:", min_y, "to", max_y, "\n")
  cat("Files saved:", paste0(output_path, ".shp"), "and", paste0(output_path, ".geojson"), "\n")
  
  return(rectangle_projected)
}

Use function to create shapefile and geojson

Code

# Create the rectangle shapefile
dingo_extent <- create_rectangle_shapefile(
  df = dingo_data,
  x_col = "x",             # column name for easting/longitude
  y_col = "y",             # column name for northing/latitude
  buffer = 0.1,           # buffer distance in coordinate units (degrees or m)
  crs = 4326,              # coordinate reference system of GPS locations (will output in 4326)
  output_path = "spatial_layers/extent/dingo_extent" # without extension
)

Rectangle created with bounds:
X range: 129.7193 to 131.4602 
Y range: -20.95433 to -20.09433 
Files saved: spatial_layers/extent/dingo_extent.shp and spatial_layers/extent/dingo_extent.geojson

Code

# View the result
print(dingo_extent)

Simple feature collection with 1 feature and 2 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 129.7194 ymin: -20.95433 xmax: 131.4602 ymax: -20.09433
Geodetic CRS:  WGS 84
  id     area                       geometry
1  1 1.497153 POLYGON ((129.7193 -20.9543...

Code

# Create a sf object
dingo_extent_sf <- st_geometry(dingo_extent)

# Optional: Add your original points to the plot
plot(dingo_extent_sf, main = "Rectangle with Original Points")
plot(st_geometry(dingo_data_sf), add = TRUE, col = "red")

Check DEM

We downloaded the DEM from Geoscience Australia, which is a 1 second resolution DEM for Australia. We will use this to create slope and roughness layers. We will .

Code

# read in the DEM
dem <- terra::rast("spatial_layers/topography/Hydro_Enforced_1_Second_DEM.tif") # hydro-enforced DEM
dem

class       : SpatRaster 
dimensions  : 3096, 6267, 1  (nrow, ncol, nlyr)
resolution  : 0.0002777778, 0.0002777778  (x, y)
extent      : 129.7193, 131.4601, -20.95431, -20.09431  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326) 
source      : Hydro_Enforced_1_Second_DEM.tif 
name        : Hydro_Enforced_1_Second_DEM

Code

plot(dem)

Create slope layer

Code

# # create slope layer
# slope <- terra::terrain(dem, v = "slope", unit = "degrees", neighbors = 8)
# plot(slope)
# # save the layer
# terra::writeRaster(slope, "spatial_layers/topography/slope.tif", overwrite = T)

# load slope
slope <- terra::rast("spatial_layers/topography/slope.tif")
plot(slope)

Clip the spatial layers to the dingo extent

Code

# clip artificial food layer
artificial_food_clipped <- st_intersection(artificial_food, dingo_extent_sf)

Warning: attribute variables are assumed to be spatially constant throughout
all geometries

Code

# clip artificial water layer
artificial_water_clipped <- st_intersection(artificial_water, dingo_extent_sf)

Warning: attribute variables are assumed to be spatially constant throughout
all geometries

Code

# clip the roads layer
roads_clipped <- st_intersection(roads, dingo_extent_sf)

Warning: attribute variables are assumed to be spatially constant throughout
all geometries

Plot the clipped layers

Code

ggplot() +
  geom_sf(data = roads_clipped, color = "black") +
  geom_sf(data = dingo_data_sf, aes(color = as.factor(dog_id)), size = 0.5, alpha = 0.25) +
  geom_sf(data = artificial_water_clipped, color = "blue") +
  geom_sf(data = artificial_food_clipped, color = "red") +
  scale_colour_viridis_d("Dingo ID") +
  theme_bw() +
  theme(legend.position = "none")

Code

ggsave("figures/gps_locations_vector_objects.png", width = 150, height = 100, units = "mm", dpi = 600)

Create distance covariates

Code

# # create distance to artificial food layer
# artificial_food_raster <- terra::rasterize(vect(artificial_food_clipped), dem, field = 1, background = NA)
# dist_artificial_food <- terra::distance(artificial_food_raster)
# plot(dist_artificial_food, main = "Distance to Artificial Food")
# terra::writeRaster(dist_artificial_food, "spatial_layers/artificial_food/dist_artificial_food.tif", overwrite = T)
# 
# # create distance to artificial water layer
# artificial_water_raster <- terra::rasterize(vect(artificial_water_clipped), dem, field = 1, background = NA)
# dist_artificial_water <- terra::distance(artificial_water_raster)
# plot(dist_artificial_water, main = "Distance to Artificial Water")
# terra::writeRaster(dist_artificial_water, "spatial_layers/artificial_water/dist_artificial_water.tif", overwrite = T)
# 
# # create distance to roads layer
# roads_raster <- terra::rasterize(vect(roads_clipped), dem, field = 1, background = NA)
# dist_roads <- terra::distance(roads_raster)
# plot(dist_roads, main = "Distance to Roads")
# terra::writeRaster(dist_roads, "spatial_layers/roads/dist_roads.tif", overwrite = T)

Load distance layers

Code

# load distance to artificial food layer
dist_artificial_food <- terra::rast("spatial_layers/artificial_food/dist_artificial_food.tif")
plot(dist_artificial_food, main = "Distance to Artificial Food")

Code

# load distance to artificial water layer
dist_artificial_water <- terra::rast("spatial_layers/artificial_water/dist_artificial_water.tif")
plot(dist_artificial_water, main = "Distance to Artificial Water")

Code

# load distance to roads layer
dist_roads <- terra::rast("spatial_layers/roads/dist_roads.tif")
plot(dist_roads, main = "Distance to Roads")

Load packages

Load data

Load shapefiles

Plot the shapefiles with dingo GPS data

Create an AOI for downloading DEM

Use function to create shapefile and geojson

Check DEM

Create slope layer

Clip the spatial layers to the dingo extent

Plot the clipped layers

Create distance covariates

Load distance layers

References